Transcriptomics

Dataset Information

0

RNA editing in cancer impacts mRNA abundance in immune response pathways


ABSTRACT: RNA editing generates modifications to RNA sequences, thereby increasing protein diversity and shaping various layers of gene regulation. Recent studies have revealed global shifts in editing levels across many cancer types, as well as a few specific mechanisms implicating individual sites in tumorigenesis or metastasis. However, most tumor-associated sites, predominantly in noncoding regions, have unknown functional relevance. Here, we carry out integrative analysis of RNA editing profiles between epithelial (E) and mesenchymal (M) tumors, since epithelial-mesenchymal transition (EMT) is a key paradigm for metastasis. We identify distinct editing patterns between E and M tumors in seven cancer types using TCGA data, an observation further supported by single-cell RNA-seq data and ADAR perturbation experiments in cell culture. Through computational analyses and experimental validations, we show that differential editing sites between E and M phenotypes function by regulating mRNA abundance of their respective genes. Our analysis of >120 RNA-binding proteins revealed ILF3 as a potential regulator of this process, supported by experimental validations. Consistent with the known roles of ILF3 in immune response, E-M differential editing sites are enriched in genes involved in immune and viral processes. The strongest target of editing-dependent ILF3 regulation is the transcript encoding PKR, a crucial player in immune and viral response. Our study reports widespread differences in RNA editing between epithelial and mesenchymal tumors and a novel mechanism of editing-dependent regulation of mRNA abundance. It reveals the broad impact of RNA editing in cancer and its relevance to cancer-related immune pathways.

ORGANISM(S): Homo sapiens

PROVIDER: GSE147487 | GEO | 2020/09/17

REPOSITORIES: GEO

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

2020-06-30 | MODEL2004040001 | BioModels
2018-04-25 | GSE105773 | GEO
2019-09-23 | MODEL1909230002 | BioModels
2020-05-09 | GSE130310 | GEO
2018-03-01 | E-MTAB-6042 | biostudies-arrayexpress
2014-12-19 | E-GEOD-62917 | biostudies-arrayexpress
2015-02-11 | ST000282 | MetabolomicsWorkbench
2014-06-02 | PXD001007 | Pride
2011-09-21 | GSE28040 | GEO
2014-05-23 | E-GEOD-57910 | biostudies-arrayexpress